Summary
The human gut microbiome is a complex ecosystem, which contributes essential functions to human physiology. Changes to the microbiome are associated with several chronic diseases characterised by inflammation, including neurodegenerative and autoimmune diseases. Microbiome-derived effector molecules comprising nucleic acids, (poly)peptides and metabolites are present at high levels in the gut but have so far eluded systematic study. This gap in knowledge is limiting mechanistic understanding of the microbiome’s functional impact on chronic diseases such as Parkinson’s disease (PD) and rheumatoid arthritis (RA). Here, I will for the first time integrate a combination of advanced high-resolution methodologies to comprehensively identify the constituents of this molecular complex and their impact on the human immune system. First, I will perform a quantitative, integrated multi-omic analysis on microbiome samples collected from healthy individuals and patients with newly diagnosed PD or RA. I will integrate and analyse the data using a newly developed knowledge base. Using contextualised prior knowledge (ExpoBiome Map) and machine learning methods, I will identify microbial molecules associated with condition-specific immunophenotypes. Second, I will validate and track the biomarker signature during a model clinical intervention (therapeutic fasting) to predict treatment outcomes. Third, microbes and molecules will be screened in personalised HuMiX gut-on-chip models to identify novel anti-inflammatory compounds. By providing mechanistic insights into the molecular basis of human-microbiome interactions, the project will generate essential new knowledge about causal relationships between the gut microbiome and the immune system in health and disease. By facilitating the elucidation of currently unknown microbiome-derived molecules, it will identify new genes, proteins, metabolites and host pathways for the development of future diagnostic and therapeutic applications.
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More information & hyperlinks
Web resources: | https://cordis.europa.eu/project/id/863664 |
Start date: | 01-11-2020 |
End date: | 31-10-2025 |
Total budget - Public funding: | 1 998 620,00 Euro - 1 998 620,00 Euro |
Cordis data
Original description
The human gut microbiome is a complex ecosystem, which contributes essential functions to human physiology. Changes to the microbiome are associated with several chronic diseases characterised by inflammation, including neurodegenerative and autoimmune diseases. Microbiome-derived effector molecules comprising nucleic acids, (poly)peptides and metabolites are present at high levels in the gut but have so far eluded systematic study. This gap in knowledge is limiting mechanistic understanding of the microbiome’s functional impact on chronic diseases such as Parkinson’s disease (PD) and rheumatoid arthritis (RA). Here, I will for the first time integrate a combination of advanced high-resolution methodologies to comprehensively identify the constituents of this molecular complex and their impact on the human immune system. First, I will perform a quantitative, integrated multi-omic analysis on microbiome samples collected from healthy individuals and patients with newly diagnosed PD or RA. I will integrate and analyse the data using a newly developed knowledge base. Using contextualised prior knowledge (ExpoBiome Map) and machine learning methods, I will identify microbial molecules associated with condition-specific immunophenotypes. Second, I will validate and track the biomarker signature during a model clinical intervention (therapeutic fasting) to predict treatment outcomes. Third, microbes and molecules will be screened in personalised HuMiX gut-on-chip models to identify novel anti-inflammatory compounds. By providing mechanistic insights into the molecular basis of human-microbiome interactions, the project will generate essential new knowledge about causal relationships between the gut microbiome and the immune system in health and disease. By facilitating the elucidation of currently unknown microbiome-derived molecules, it will identify new genes, proteins, metabolites and host pathways for the development of future diagnostic and therapeutic applications.Status
SIGNEDCall topic
ERC-2019-COGUpdate Date
27-04-2024
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